s2fieldsofstudy: Biology Medicine Chemistry

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Dihydroartemisinin attenuates pulmonary inflammation and fibrosis in rats by suppressing JAK2/STAT3 signaling

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has induced a worldwide pandemic since early 2020. COVID-19 causes pulmonary inflammation, secondary pulmonary fibrosis (PF); however, there are still no effective treatments for PF. The present study aimed to explore the inhibitory effect of dihydroartemisinin (DHA) on pulmonary inflammation and PF, and its molecular mechanism. Morphological changes and collagen deposition were analyzed using hematoxylin-eosin staining, Masson staining, and the hydroxyproline content. DHA attenuated early alveolar inflammation and later PF in a bleomycin-induced rat PF model, and inhibited the expression of interleukin (IL)-1β, IL-6, tumor necrosis factor α (TNFα), and chemokine (C-C Motif) Ligand 3 (CCL3) in model rat serum. Further molecular analysis revealed that both pulmonary inflammation and PF were associated with increased transforming growth factor-β1 (TGF-β1), Janus activated kinase 2 (JAK2), and signal transducer and activator 3(STAT3) expression in the lung tissues of model rats. DHA reduced the inflammatory response and PF in the lungs by suppressing TGF-β1, JAK2, phosphorylated (p)-JAK2, STAT3, and p-STAT3. Thus, DHA exerts therapeutic effects against bleomycin-induced pulmonary inflammation and PF by inhibiting JAK2-STAT3 activation. DHA inhibits alveolar inflammation, and attenuates lung injury and fibrosis, possibly representing a therapeutic candidate to treat PF associated with COVID-19.

AGING deaths worldwide. SARS-CoV-2 crosses borders and races, and infects patients of any age via person-toperson transmission [2]. The disease causes a wide range of symptoms, from asymptomatic to respiratory failure [3,4]. Pulmonary fibrosis (PF) is a possible complication of pulmonary involvement in COVID-19, which is a chronic, progressive, and fatal form of fibrosing interstitial pneumonia [5,6].
The pathophysiological characteristics of PF are the inability of damaged alveolar epithelial cells to reconstruct normally, excessive deposition of collagen (and other extracellular matrix components), and the persistent presence of fibroblasts, leading to the destruction of the normal lung structure [5]. The interstitial matrix widens during the progression of PF and the normal pulmonary parenchyma becomes compressed, leading to respiratory failure.
Radiological imaging is the fastest and most direct method to assess pulmonary parenchymal involvement [7][8][9]. Computed tomography (CT) is an important modality to evaluate PF. Some studies analyzed the chest CT characteristics of patients with COVID-19, and patchy ground-glass opacities were the most common changes [7]. The results of further imaging studies for COVID-19 further confirmed that PF might be one of the major complications in patients with COVID-19 [6,10,11].
PF comprises the formation of heterogeneous pulmonary lesions by the transformation of abnormal pulmonary epithelial cells and pulmonary interstitial cells into invasive pulmonary myofibroblasts, resulting in pulmonary parenchymal fibrosis. The specific mechanism of the origin and activation of invasive pulmonary myofibroblasts is still unknown. It is likely to be a multi-factor and multi-step process, including the recruitment of circulating fibroblasts' blood mesenchymal precursors, activation of fibroblasts [12]. The process of PF involves the abnormal expression of multiple cytokines and the activation of many signaling pathways. Previous studies found that the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathways are involved in idiopathic pulmonary fibrosis (IPF) [13,14]. The levels of activated JAK2 and STAT3 were elevated in alveolar type II epithelial cells (ATII) and lung fibroblasts from patients with IPF, in which JAK2 and STAT3 participate in lung fibrosis by dependent and independent mechanisms [12]. In addition, fibroblasts deficient in STAT3 were less sensitive to the pro-fibrotic effects of transforming growth factor beta 1 (TGF-β1) [15]. All of these studies suggested that STAT3 and its signaling pathways play an important role in PF.
STAT comprises an oncoprotein family encoded by tumor-associated genes and includes seven members. STAT3 can transduce peptide hormone signals from the cell surface to the nucleus and is activated by cytokines, growth factors, and many peptide hormones [16]. Upon binding of extracellular ligands to receptors on the surface of the cell membrane to form cytokine-receptor kinase complexes or growth factor-receptor complexes. These complexes can phosphorylate the intracellular kinases of the JAK or SRC families, then the activated JAK or SRC families can phosphorylate Tyr705 sites to activate the STAT3 [16].
Targeted inhibition of the STAT3 signaling pathway is expected to inhibit the formation of PF [16,17]. For example, cryptotanshinone protects against PF through inhibiting STAT3 signaling pathway [18], AAV1.SERCA2a gene therapy reverses PF by blocking the STAT3 pathway [19]. Therefore, the development of drugs that can effectively block STAT3 phosphorylation and activation might represent one of the most promising strategies to treat PF. However, currently, there is no STAT3 inhibitor that can be used effectively and safely in the clinic. Dihydroartemisinin (DHA) is an artemisinin derivative that is used widely as an antimalarial. Accumulating evidence indicates that DHA can effectively inhibit STAT3 phosphorylation [20]. Experiments in vitro and in vivo confirmed that DHA is a potent STAT3 inhibitor, acting as an effective antimetastatic via inhibition of STAT3 phosphorylation and activation [20]. This study hypothesized that DHA could attenuate PF and thus we evaluated the effects of DHA in a rat bleomycin-induced PF model to determine its mechanism. The results of the present study could lead to a new treatment for patients with pneumonia and PF.

DHA attenuates early alveolar inflammation in a rat model of bleomycin-induced PF
At 7 days after intratracheal bleomycin (BLM) administration, with or without DHA treatment, the rats were sacrificed by inhaling excess isoflurane (n = 6/group). Lung tissue samples were removed quickly and blood samples were collected for further analysis. Alveolar inflammation was induced on day 7 after intratracheal administration of BLM. As visualized by hematoxylin and eosin (H&E) staining ( Figure 1A), the lung tissues from the control group of rats had intact alveoli, while the lung tissues from the rats stimulated with BLM exhibited obvious inflammatory cell infiltration in the alveolar cavity, pulmonary edema accompanying septal thickness, damage to the lung architecture, and alveolar disarray. DHA attenuated AGING alveolar inflammation and reduced the septal edema and alveolar damage. Lung tissues from the DHA-3 group (100 mg/kg/d) exhibited some infiltration of inflammatory cells and slight pulmonary edema in comparison to those in the BLM group.
The Szapiel scoring method was used to assess the extent of pulmonary inflammation; the scoring rules are shown in Table 1. Three sections per specimen were stained and evaluated for Szapiel scoring, and two independent pathologists, who were blinded to the experiment, assessed the results. Compared with that of the control group, the Szapiel scores of BLM group increased significantly. Compared with that of the BLM group, the Szapiel score of the DHA-2 (50 mg/kg/d) group and DHA-3 group decreased significantly ( Figure  1B). In the early stage of the PF model, BLM caused pneumonia, and DHA effectively inhibited pulmonary  Severe alveolar inflammation, involving more than 50% of the lung 3 inflammation. In addition, DHA inhibited pulmonary inflammation in a dose-dependent manner, with intraperitoneal injection of a high dose of DHA3 inhibiting alveolar inflammation significantly.

DHA inhibits the inflammatory response in the lungs and serum of BLM-exposed rats
To reveal the role of DHA in the early inflammation of the BLM rat model, we used an enzyme-linked immunosorbent assay (ELISA) and quantitative realtime reverse transcription PCR (qRT-PCR) to detect inflammatory cytokines in the serum and lungs of rats at 7 days after modeling. The qRT-PCR result demonstrated that stimulation with BLM significantly increased the mRNA levels of interleukin (Il)-1B, Il6, tumor necrosis factor α (Tnfa), and chemokine (C-C Motif) Ligand 3 (Ccl3) in the rat lungs, while DHA attenuated these increases significantly (Figure 2A-2D) in a dose-dependent manner: A high dose of DHA3 inhibited the transcription of all the tested inflammatory cytokines in lung tissue effectively. Consistent with the qRT-PCR results, the protein levels of IL-1β, IL-6, TNFα, and CCL3 in the rat serum increased after BLM stimulation but decreased after DHA treatment ( Figure  2E-2H). These results indicated that DHA attenuated the inflammatory response in the lungs and serum of BLM-exposed rats. AGING DHA reduces the inflammatory response in the lungs via JAK2/STAT3 signaling To explore the mechanism by which DHA inhibits the early alveolar inflammatory response in the rat BLMinduced PF model, we evaluated the Tgfb1, Jak2, and Stat3 mRNA expression and TGF-β1, JAK2, p-JAK2, STAT3, and p-STAT3 protein levels in lung tissues at day 7 after intratracheal administration of BLM, with and without DHA treatment, using qRT-PCR and immunohistochemistry staining. In homogenized lung tissue, the Tgfb1, Jak2, and Stat3 mRNA levels in the BLM group were higher than those in the control groups. DHA1 (25 mg/kg/d) inhibited the expression of Tgfb1 mRNA significantly ( Figure 3A), DHA3 inhibited the expression of Jak2 mRNA significantly AGING ( Figure 3B), and DHA2 inhibited the expression of Stat3 mRNA significantly ( Figure 3C). The IHC results also showed that BLM increased the levels of TGF-β1, JAK2, p-JAK2, STAT3, and p-STAT3 in lung tissues ( Figure 3D-3E, Figure 4A-4C), and the mean integrated optical density (IOD) values showed that DHA reduced the levels of TGF-β1, JAK2, p-JAK2, STAT3, and p-STAT3 proteins in a dosedependent manner ( Figure 3F-3G, Figure 4D-4F). DHA could inhibit the phosphorylation of JAK2, thereby inhibiting the phosphorylation of STAT3. Thus, DHA might reduce the inflammatory response of the lungs by inhibiting the activation of JAK2/STAT3 signaling.

DHA reduces PF induced by BLM in rats
On day 28 after intratracheal administration of BLM, other rats were sacrificed by inhaling excess isoflurane (n = 6/group). H&E staining was performed to explore the pathological alterations and Masson staining was used to evaluate the collagen fibrils in the lung tissues., Compared with those of the control group, under the microscope, the lung tissues in the BLM group showed loss of alveolar architecture, pulmonary septal thickening, and increased cell number, ultimately resulting in severe lung tissue damage. However, DHA attenuated the pathological alterations in the lung tissues at a dose of DHA2 ( Figure 5A). The Szapiel score also showed that DHA inhibited alveolar inflammation, with the Szapiel score decreasing in a AGING DHA dose-dependent manner ( Figure 5B). ELISA was used to explore the inflammatory cytokines in the serum of all rats, which demonstrated significantly higher cytokine levels in BLM group than those in the control group. By contrast, the IL-1β, IL-6, TNFα, and CCL3 levels in serum were reduced by DHA in a dose dependent manner, above 50 mg/kg/d suppressed all the tested inflammatory cytokines levels, and all doses of DHA reduced CCL3 levels ( Figure 5C-5F).
Masson staining showed massive deposits of collagen in the pulmonary septum (blue) of lung tissues in the BLM group, and DHA treatment reduced the size of the collagen deposits ( Figure 6A). The Ashcroft score was  The area of fibrous tissue mass is less than 10% of the lung, with mild lung structural damage 4 5 The area of fibrous tissue mass is 10-50% of the lung, and with pulmonary structural damage 5 6 The area of fibrous tissue mass is more than 50% of the lung, and with obvious pulmonary structural damage 6   7 Severe lung damage, large areas of fibrosis, honeycomb lung 7 8 Full field fibrous tissue 8 used to evaluate the pathological alterations of PF ( Table 2). The Ashcroft score of the BLM group was increased significantly, and DHA decreased the Ashcroft score in a dose-dependent manner Figure 6B). The hydroxyproline content in the lung tissue was measured to evaluate PF. At 28 days after BLM treatment, the rats demonstrated a higher hydroxyproline content than that in the control group. Compared with that of the BLM group, DHA reduced the hydroxyproline content significantly in a dosedependent manner ( Figure 6C).

DHA inhibits PF via JAK2/STAT3 signaling
TGF-β1 is known to be critical for PF; therefore, we used qRT-PCR to detect Tgfb1 mRNA expression in the lung tissue of all rat groups. The results confirmed that the expression of Tgfb1 mRNA was higher than that in the control group on day 28 after BLM injection. However, DHA2 could significantly reduce this increase ( Figure 6D). We also detected the mRNA of Jak2 and Stat3 in the rat lung tissues. Similar to the results for Tgfb1, high dose DHA2 could decrease the expression of Jak2 and Stat3 mRNA significantly ( Figure 6E and 6F).

DISCUSSION
PF is a common pathological process after lung tissue injury, and chronic relapses of PF affects the quality of life for patients, even causing respiratory failure. PF occurs after severe pulmonary infection caused by SARS-CoV-2 [21]. Currently, COVID-19 is prevalent worldwide; however, there is still no effective drug treatment for PF. Therefore, exploring the mechanism of PF to find effective drugs is expected to improve the quality of life for patients with COVID-19.
Pulmonary inflammation is the initial cause of PF [21]. During the inflammatory response, the alveolar epithelium and vascular endothelium are damaged initially, and inflammatory cells and immune effector cells enter the lung to release inflammatory mediators, resulting in the formation of alveolar exudates, including a variety of inflammatory cytokines [22]. In the present study, in the early stage of rat BLM-induced PF model, the mRNA and protein levels of inflammatory factors IL-1β, IL-6, TNFα, and CCL3 in the lung tissues and serum were increased significantly.
CCL3 is expressed on the surface of macrophages and can directly interfere with the secretion of TGF-β1, thus regulating the course of acute pulmonary inflammation [23]. Normally, the expression of CCL3 is low; however, when the target cells are stimulated by endotoxins, such as lipopolysaccharide, viral proteins, or AGING proinflammatory factors (IL-1β), the related cell signaling pathway is activated, and CCL3 expression increases. The earliest reports of COVID-19 clarified that IL-1β, IL-6, TNFα, and CCL3 in the peripheral blood of patients with severe COVID-19 pneumonia were significantly higher than those of non-severe patients, and this cytokine storm is an important factor contributing to death from COVID-19 [24]. In addition, our study found that IL-1β, IL-6, TNFα, and CCL3 levels in rat blood increased significantly during the acute inflammation stage of PF induced by BLM. However, DHA could reduce the inflammatory response in the lungs of the model rats significantly and reduced the levels of cytokines, such as CCL3, in the blood. AGING Previous studies have shown that CCL3 and its receptors promote fibrosis [23], and analysis of alveolar lavage fluid in patients with congenital PF showed an increased level of CCL3. Our study also found that the CCL3 level in the blood of rats with PF increased significantly. However, the CCL3 level in the blood of rats treated with DHA2 decreased significantly. These findings suggested that CCL3 plays an important role in pulmonary inflammation and PF, and that DHA inhibits CCL3 expression to attenuate pulmonary inflammatory responses and PF.
Cytokines can either directly activate myofibroblasts or induce the transformation of epithelial cells into AGING fibroblasts, leading to the formation of fibrosis [25]. Furthermore, many cytokines, particularly the IL-6 family, can activate STAT3 rapidly within cells. Persistent STAT3 activation results in chronic inflammation and fibrosis [16].
In the early stage of BLM-induced PF, we observed the activation of the JAK2/STAT3 signaling pathway [26]. JAK/STAT signaling induces macrophage activation, and the activated macrophages secrete cytokines, such as platelet-derived factor, TNFα, IL-28, and nitric oxide. TNFα induces the expression of TGF-β directly [27]; therefore, in the early stage of the BLM-induced rat model, the expression of TGF-β1 was also high in lung tissue. TGF-β1 is a key factor for fibrosis that can promote the transformation of pulmonary fibroblasts into myofibroblasts, produce more matrix protein components, and release them into the extracellular matrix, leading to PF [28,29]. Furthermore, TGF-β1 signaling can induce the phosphorylation and activation of JAK2, thereby activating the JAK2/STAT3 signaling pathway, and this vicious circle amplifies the inflammatory response and PF [26]. Our results showed that in the later stage of the PF model induced by BLM, Masson staining confirmed the presence of PF, and IHC staining verified the activation of JAK2/STAT3 signaling. Our findings suggested that JAK2/STAT3 signaling pathways play a crucial role in the early stage of the inflammatory response in the BLM-induced PF model, as well as in the later stage of PF. Previous studies have detected JAK2/p-JAK2 and STAT3/pSTAT3 levels using quantitative real time-PCR, western blotting, and immunohistochemistry, and the levels of these proteins were observed to be upregulated in the lung tissues of patients with IPF, which demonstrated that phosphorylated STAT3 participates in both lung epithelial cell damage and the fibroblast to myofibroblast transition, making STAT3 an attractive therapeutic target in PF [12,26]. Some researchers have attempted to interfere with STAT3 activation and associated signaling as a strategy to ameliorate PF, and they found that a cell-permeable peptide inhibitor of STAT3 phosphorylation or STAT3 associated signaling pathway inhibitors might be a feasible therapeutic option for PF [30,31].
A growing number of STAT3 inhibitors have been developed and tested. Artemisinin is an active component extracted from the leaves and buds of Artemisia annua, a traditional Chinese medicine plant, and is a sesquiterpene lactone compound containing internal peroxides. Used in the treatment of various types of malaria, with high efficiency, Artemisinin (or its derivative DHA) induces few side effects and has other beneficial characteristics [32]. DHA has many pharmacological effects on viral infection [33], inflammation [34], and tumor proliferation [35]. In addition, DHA also shows a relatively good safety profile.
Previous studies have found that DHA can effectively block the phosphorylation of STAT3, thereby inhibiting its activation, with negligible toxicities [36]. In addition, DHA has been reported to exert protective effects against lung inflammation [37,38]. The present study revealed the role of DHA in the early inflammatory response in a rat model of BLM-induced PF. DHA effectively alleviated lung tissue damage and inhibited the release of inflammatory mediators. Notably, DHA attenuated pulmonary inflammatory responses in a dosedependent manner, such that lung tissues from the rats treated with 100 mg/kg/d of DHA exhibited only slight infiltration of inflammatory cells and little pulmonary edema, suggesting a therapeutic effect on BLM-induced acute lung injury. Our findings suggested that DHA is a potential therapeutic agent for patients with PF. Further experiments demonstrated that DHA reduced lung inflammation and lung tissue damage by inhibiting the phosphorylation of JAK2 and STAT3. The levels of phosphorylated JAK2 and phosphorylated STAT3 were reduced significantly in lung tissue by daily injection of 100 mg/kg DHA. In the late stage of the BLM-induced PF, we also found that DHA inhibited collagen deposition significantly in the lung tissues of the rat model, as well as the content of hydroxyproline, indicating that DHA inhibited the formation of PF. Further experiments showed that DHA inhibited TGF-β1 expression and the phosphorylation of JAK2 and STAT3 significantly.
The effect of DHA on JAK2 has been studied [39]; however, the present study further revealed that DHA inhibited STAT3 phosphorylation by inhibiting JAK2 phosphorylation. Regarding the mechanism by which DHA inhibits JAK2 phosphorylation, our study found that TGF-β1 expression and TNFα expression decreased significantly in lung tissues after treatment with DHA.

CONCLUSIONS
We concluded that DHA might reduce the expression of TNF-α, IL-6, and TGF-β1, and inhibit the inflammatory response and PF through the TGF-β/JAK2/STAT3 signaling pathway. The COVID-19 pandemic is causing severe health, economic, and social challenges to the global population. Therefore, it is important to anticipate and prepare for this challenge. PF is an adverse outcome of many viral infections, leading to acute respiratory distress syndrome. Currently, there is a lack of effective and safe drugs with antiviral, antiinflammatory, and anti-fibrosis properties. DHA has the potential to treat COVID-19-associated PF and prevent the long-term consequences of fibrosis that the AGING epidemic may cause. Finally, we hope that our findings will lead to the development of drugs that can prevent or treat COVID-19 induced PF.

Ethics statement
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Yang Zhou University (YZU-EC-202112004, Jiangsu, China). The experiments were performed in accordance with the requirements of the ARRIVE (Animal Research: Reporting In Vivo Experiments) guideline. Rats were anesthetized with isoflurane, and all necessary efforts were taken to minimize suffering prior to the experiments.

Animal experiments
Five-week-old male Wistar rats weighing 110-130 g were used in all experiments. The rats were bought from the Comparative Medicine Centre of Yangzhou University (Yang Zhou, Jiangsu, China). The rats were bred under pathogen-free conditions with a 12-hour dark/light cycle, in a laminar flow cabinet at 24°C and a relative humidity of 40-60%. Rats were provided with food and purified water, and were allowed 1 week to adapt to the environmental before the experiment.
The rats were randomly divided into five groups (n = 12 in each group) as follows: 1. The BLM group received bleomycin (BLM; 5 mg/kg) via intratracheal instillation, BLM was dissolved in 0.9% NaCl at a concentration of 4 mg/ml; 2. The Control group, who received the same volume of 0.9% NaCl solution via intratracheal installation only; 3. DHA groups received intratracheal instillation of BLM, then received daily intraperitoneal injection of DHA from second day. The DHA1 group received daily intraperitoneal injection of DHA1 (25 mg/kg/d), DHA was dissolved in dimethyl sulfoxide (DMSO) at a concentration of 30 mg/ml; 4. The DHA2 group, who received intratracheal instillation of BLM and daily intraperitoneal injection of DHA2 (50 mg/kg/d), DHA was dissolved in DMSO at a concentration of 60 mg/ml; and 5. The DHA3 group, who received intratracheal instillation of BLM and daily intraperitoneal injection of DHA3 (100 mg/kg/d), DHA was dissolved in DMSO at a concentration of 120 mg/ml. To exclude the intervention effect of DMSO on pneumonia and PF, we adjusted the concentration of DHA to ensure equal doses of DMSO in each group. BLM was injected directly into the trachea under direct vision after the trachea had been exposed through a midline anterior neck incision.

Hydroxyproline and collagen measurement
The content of hydroxyproline was measured to calculate the total collagen content in lung tissue. Hydroxyproline determination kit (A030-2-1) was purchased from Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China), which was operated in strict compliance with the manufacturer's instructions. All experiments were performed in triplicate. Data for the hydroxyproline content are expressed as nanograms of hydroxyproline per milligrams of lung tissue (ng/mg lung).

RNA extraction and quantitative real-time reverse transcription PCR (qRT-PCR)
Total RNA was extracted from lung tissue using a RNeasy Mini Kit (Invitrogen, Waltham, MA, USA). A reverse transcription kit (Takara, Shiga, Japan) was used to perform first-strand cDNA synthesis, and an iQ5 Multicolor Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) was used to conduct the quantitative real-time PCR, using SYBR Green dye (Roche Diagnostics, Mannheim, Germany). The Gapdh (glyceraldehyde-3-phosphate dehydrogenase) gene was used as an internal control to calculate the relative fold expression levels. The quantitative real-time PCR was performed in triplicate. Primers for Il1B (IL-1β), Il6, Tnfa (TNFα), Ccl3, Tgfb1 (TGF-β1), Jak2, Stat3, and Gapdh are shown in Table 3.

Histological analysis
H&E and Masson staining were used to analyze the grades of pulmonary inflammation and fibrosis. Lung tissues were fixed using 4% paraformaldehyde for 24 h at room temperature and then embedded in paraffin. Sections for pathological analysis were cut at 4 μm thickness and stained with H&E, Masson staining, and using immunohistochemistry. Masson's trichrome was purchased from Beijing Solarbio Science and Technology, Ltd., (GB1340, Solarbio, Beijing, China). An optical microscope was used to observe the H&E and Masson stained sections and lung injury was scored by two pathologists who were blinded to the treatment. The severity of pulmonary inflammation was scored using the Szapiel scoring method [40] and PF was scored using the Ashcroft scoring system [41]; (Tables  1 and 2).

Western blotting
The proteins extracted from tissues were separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis and the separated proteins were transferred onto a nitrocellulose membrane (GE Healthcare Life Sciences, Pittsburgh, PA, USA). Blots were probed with anti-TGF-β1, anti-JAK2, anti-p-JAK2, anti-STAT3, anti-p-STAT3, or anti-GAPDH primary antibodies at a dilution of 1:2,000. The anti-GAPDH antibody (bs-0755R) was provided by Bioss (Beijing, China). The secondary HRPconjugated antibody was also used at a dilution of 1:2,000. The West Pico chemiluminescent Substrate (Pierce, Carlsbad, CA, USA) was used to visualize the protein bands and densitometric image analysis software (Image Master VDS; Pharmacia Biotech) was used to quantify the protein bands. All assays were performed in triplicate.

Statistical analysis
SPSS 20.0 (IBM Corp., Armonk, NY, USA) was used to conduct the statistical analysis. The means ± standard error was used to express the continuous variables, oneway analysis of variance (ANOVA) and Dunnett's t test was used for comparisons between groups based on the normal distribution of the data. Values of P < 0.05 were regarded as statistically significant ( * P < 0.05, ** P < 0.001).

AUTHOR CONTRIBUTIONS
XLY and HMD conceived and designed the experiments. XLY, XYJ, KJJ, XJZ, TG, JJB, XWZ, and HMD performed the experiments. CMZ and HMD analyzed the data. XLY wrote the paper. XLY and HMD supervised the study. All authors read and approved the final manuscript.


In-Depth Satellitome Analyses of 37 Drosophila Species Illuminate Repetitive DNA Evolution in the Drosophila Genus

Abstract Satellite DNAs (SatDNA) are ubiquitously present in eukaryotic genomes and have been recently associated with several biological roles. Understanding the evolution and significance of SatDNA requires an extensive comparison across multiple phylogenetic depths. We combined the RepeatExplorer pipeline and cytogenetic approaches to conduct a comprehensive identification and analysis of the satellitome in 37 species from the genus Drosophila. We identified 188 SatDNA-like families, 112 of them being characterized for the first time. Repeat analysis within a phylogenetic framework has revealed the deeply divergent nature of SatDNA sequences in the Drosophila genus. The SatDNA content varied from 0.54% of the D. arizonae genome to 38.8% of the D. albomicans genome, with the SatDNA content often following a phylogenetic signal. Monomer size and guanine–cytosine-content also showed extreme variation ranging 2–570 bp and 9.1–71.4%, respectively. SatDNA families are shared among closely related species, consistent with the SatDNA library hypothesis. However, we uncovered the emergence of species-specific SatDNA families through amplification of unique or low abundant sequences in a lineage. Finally, we found that genome sizes of the Sophophora subgenus are positively correlated with transposable element content, whereas genome size in the Drosophila subgenus is positively correlated with SatDNA. This finding indicates genome size could be driven by different categories of repetitive elements in each subgenus. Altogether, we conducted the most comprehensive satellitome analysis in Drosophila from a phylogenetic perspective and generated the largest catalog of SatDNA sequences to date, enabling future discoveries in SatDNA evolution and Drosophila genome architecture.

Introduction
Virtually all eukaryotic genomes studied to date contain tandem arrays in which the basic units or monomers are repeated in a head-to-tail fashion known as satellite DNAs (SatDNA) (Charlesworth et al. 1994). SatDNA can comprise up to 50% of eukaryotic genomes and are usually found in long tandemly repeated arrays that can span up to megabases (reviewed by Schmidt and Heslop-Harrison 1998;Plohl et al. 2012). SatDNA abundance can differ dramatically among species, appearing to evolve by array expansion and shrinkage of related repeat variants (Nijman and Lenstra 2001;Slamovits et al. 2001). In Drosophila species, SatDNA can account for more than 30% of the genome, and amplification/contraction events of distinct SatDNA families have been identified as a crucial factor in shaping the architecture and size of the Drosophila genome (Bosco et al. 2007). Moreover, species evolution may be associated with SatDNA given that rapid changes in copy number can trigger rapid genome changes (Dover and Tautz 1986;Gregory and Johnston 2008;Ferree and Barbash 2009;Brand and Levine 2021).
SatDNA are often associated with heterochromatin and the low gene content in these regions led to the misconception that SatDNA have no essential function, being labeled as "junk" DNA (Ohno 1972). However, it is now clear that SatDNA sequences can regulate cellular processes such as kinetochore assembly, X chromosome recognition, and meiotic chromosome segregation (Dernburg et al. 1996;Kuhn 2015;Shatskikh et al. 2020). For example, recent publications have demonstrated that the 1.688 SatDNA family in D. melanogaster is crucial for centromeric function, chromosome missegregation in hybrids, dosage compensation, and heterochromatin formation (Cattani and Presgraves 2012;Ferree and Prasad 2012;Menon et al. 2014;Rošićet al. 2014). These data have strengthened the argument that SatDNA can play a significant role in genomic organization and species adaptation.
Despite their importance for genome organization, function, and evolution, SatDNA have been historically neglected in the Drosophila genomes and eukaryotes in general. SatDNA have been mainly utilized as taxonomic markers based on the number and morphology of signalbearing chromosomes and on the localization of signals in different species (Picariello et al. 2002;Bueno et al. 2021;Cabral-de-Mello et al. 2021), but this approach usually requires a priori knowledge of its sequences. In Drosophila, SatDNA investigations began later than in other animals such as frogs and mice, starting with the use of CsCl density gradients to characterize the most abundant sequences in D. virilis, D. melanogaster, and D. hydei genomes (Laird and McCarthy 1968;Gall et al. 1971;Gall and Atherton 1974;Barnes et al. 1978;Renkawitz 1979). During the last decades, SatDNA have been studied from a small sample of cloned repeats obtained by sequence biased methodological approaches (usually restriction digestion and/or PCR) isolated from one or few species (Brutlag et al. 1977;Waring and Pollack 1987;Bonaccorsi and Lohe 1991;Bachmann and Sperlich 1993;Kuhn et al. 1999;Kuhn et al. 2008).
The development of next-generation sequencing (NGS) technologies more than a decade ago has generated a vast body of sequencing data and new tools for genome assembly. However, due to their repetitive natures, SatDNA sequences create ambiguities when aligning and assembling NGS data (Treangen and Salzberg 2012). As a result, recent genome reports lack a curated characterization of SatDNA sequences as they rely on the repetitive sequences deposited in the nucleotide databases. Indeed, despite the large repertoire of sequenced genomes available for Drosophila species, only 59 SatDNA families from Drosophila are deposited in the Genbank and Repbase databases (last accessed July 2021; supplementary material, Supplementary Material online). The recent availability of the cluster-based de novo identification pipeline RepeatExplorer (Novák et al. 2013) allowed us to comprehensively identify an extensive collection of repetitive sequences from a given genome using large NGS datasets, fostering the characterization of new SatDNA (de Lima et al. 2017;Silva et al. 2019). Despite these advances, recent studies have focused on SatDNA dynamics in restricted phylogenetic groups such as the D. melanogaster subgroup or D. virilis subgroup (Dias et al. 2014;Larracuente 2014;Khost et al. 2017;Silva et al. 2019;de Lima et al. 2020;Sproul et al. 2020). An expanded phylogenetic analysis beyond these two subgroups would benefit our understanding of genome evolution. Thus, a representative catalog of the SatDNA families and a comprehensive approach are important steps toward understanding their evolutionary pathways and how they are linked to genome evolution.
We performed here a high-throughput analysis of the satellitome from 37 Drosophila species and their correlation with genome size. We confirmed the presence of the previously described SatDNA and identified 112 new SatDNA sequences present in Drosophila genomes. Moreover, we compared the maintenance of SatDNA families throughout Drosophila phylogeny and provide evidence for species-specific birth and amplification of new SatDNA families in the context of genome size. Altogether, our work is the most comprehensive comparison of SatDNA in Drosophila species to date that is focused on a phylogenetic perspective to generate a large database that will improve Drosophila genome annotation.

A De Novo and In Silico Identification of SatDNA-like Families Reveals Variation in the Drosophila Satellitome
We performed a de novo identification of repeats in 37 Drosophila species with the RepeatExplorer pipeline based de Lima and Ruiz-Ruano GBE on raw Illumina reads. We chose this approach to ensure the comprehensive characterization of SatDNA and other repetitive families, because methods based on assembled genomes hold potential biases due to the difficulty of assembling highly repetitive regions (Drosophila 12 Genomes Consortium 2007;Guillén et al. 2015;Kim et al. 2021). The overall de novo clustering of reads used at least 0.35-fold of the genome coverage (supplementary table S1, Supplementary Material online). This genome coverage is sufficient to identify the most repetitive elements in eukaryotic genomes (Novák et al. 2017;Fu et al. 2019;Silva et al. 2019) (see Materials and Methods). The present study expands more than 3-fold the available SatDNA dataset from 59 to 188 SatDNA-like sequences of Drosophila, including new SatDNA in several species and previously described SatDNA in additional species ( fig. 1). The overall characterization of all SatDNA sequences and the overall repetitive DNA content for 37 species are given in figure 2 and table 1; supplementary   table S2, Supplementary Material online. All consensus sequences for the SatDNA families obtained in this study are present in supplementary material S1, Supplementary Material online. We considered only clusters with genomic proportion equal to or higher than 0.01%. Furthermore, all SatDNA-like monomers were independently identified by the RepeatProfiler pipeline (Negm et al. 2020) to confirm the expected coverage depth profiles for each SatDNA-like family (supplementary material S2, Supplementary Material online). The high number of SatDNA families found in the genomes of the Drosophila species corroborates the assumption that eukaryote genomes usually contain a high diversity of SatDNA families (Melters et al. 2013). The overall repetitive DNA (i.e., all repetitive sequences above the 0.01% cut-off) comprised anywhere from 8.5% of a genome (D. erecta) to 48.1% of a genome (D. albomicans males) ( fig. 2 and table 1). We assigned clusters from all species to specific repeat types and families (supplementary material S4, Supplementary Material online), except for the D. seriema genome which showed 4% bacterial contamination and was removed by manual curation.
In general, Drosophila SatDNA differ in nucleotide sequence, complexity, motif length, abundance, and chromosome localization. SatDNA are roughly classified by motif length as simple or complex, with simple SatDNA having a motif length of 5-12 bp, whereas complex repeats are 150-360 bp (reviewed by Plohl et al. 2012). The 188 SatDNA-like families identified in the present work showed high variation for monomer length (2-570 bp) ( fig. 3A). We describe a trimodal distribution of motif length with a higher prevalence of SatDNA monomers ,50 bp (54), followed by 180-200 bp monomers (33). Although many different simple SatDNA families were identified, a major limitation of the RepeatExplorer pipeline is that high similarity among simple SatDNA might mistakenly allow clustering of unique simple SatDNA families. Simple SatDNA are known to comprise the heterochromatic regions of D. melanogaster and D. virilis subgroups species. Each of these simple SatDNA share 80-90% sequence similarity inside their respective subgroup (Jagannathan et al. 2017;Flynn et al. 2020). The D. melanogaster genome is rich in SatDNA families, most of them being (12) 7 bp or less (Lohe et al. 1993, reviewed in Lauria-Sneideman andMeller 2021). Seven out of 12 families share a similar nucleotide composition (AANAB) and are known to form long interspersed arrays (Chang and Larracuente 2017;Chang et al. 2019). Due to the clustering cut-off parameters available in RepeatExplorer (Novák et al. 2013, see Materials and Methods), simple SatDNA families in D. melanogaster are under-identified in a number of different families. However, a detailed examination of the assembled contigs generated by the RepeatExplorer pipeline shows that similar nucleotide  Russo et al. (2013) and are differentially colored by respective subgenus.
composition SatDNA families collapsed in the same cluster (supplementary table S3, Supplementary Material online). If expanded to all datasets, it is expected that the overall amount of SatDNA observed might be similar but the total number of short SatDNA families is underrepresented, indicating that k-mer approaches might be necessary to  Russo et al. (2013). The respective genome sizes (green), the non-SatDNA repetitive content (magenta), and SatDNA content (blue) are shown in the total amount of DNA comprised by each feature. D. burlai, D. leontia, D. montana, D. nasuta, and D. seriema data are not plotted due to the absence of genome size estimates for these species. One subspecies of D. mojavensis and both male and female data of D. albomicans are plotted. Genome size estimations are from the Animal Genome Size Database (www.genomesize.com), Bosco et al. (2007), Hjelmen and Johnston (2017), and Hjelmen et al. (2019).
de Lima and Ruiz-Ruano GBE identify arrays of simple SatDNA shorter than 10 bp . Notably, four nonhomologous simple SatDNA families (with monomers from 2 to 14 bp) lacking sequence similarities were correctly characterized by our methodology in the D. hydei genome when compared with previous analyses (Burgtorf and Bünemann 1994). Further, we did not identify any new SatDNA family organized in multi-unit higher-order repeats, although we confirmed the previously described pBuM-2 α/β alternating repeats of D. seriema (Kuhn et al. 2008;de Lima et al. 2017). However, future long-read sequencing approaches are necessary for a proper study of higher-order repeats (Kunyavskaya et al. 2021).
Our analysis showed that the guanine-cytosine (GC) content of SatDNA sequences in Drosophila varies widely from 9.1% to 71.8%. We observed that SatDNA sequences tend to be AT-rich, although some SatDNA sequences are very GC-rich ( fig. 3B). AT-rich SatDNA are common in insect genomes and are suggested to contribute to the duplex curvature that enhances nucleosome stability (reviewed in Palomeque and Lorite 2008). Contrastingly, higher GC content sequences are suggested to elevate double-strand breaks, base substitutions, and a high rate of deletions by DNA polymerase slippage in eukaryotes (Kiktev et al. 2018). Short-read sequencing tends to be biased toward higher GC content sequences due to PCR-based

In Silico Characterization Reveals New SatDNA Families in Drosophila
We identified a total of 188 SatDNA-like sequences in 37 species of Drosophila, 112 of them corresponding to previously undescribed SatDNA-like families distinct from sequences present in GenBank, RepBase, and SatDNA literature records (supplementary material S1, Supplementary Material online). Importantly, we characterized new SatDNA-like sequences in species without previous SatDNA identification efforts (e.g., D. burlai, D. leontia, D. malerkotliana, and D. nasuta). Other species had prior limited analysis of SatDNA, such as Strachan et al. (1985) and  4). Our results imply that SatDNA sequences are still underrepresented in databases and genome assemblies. Future cytogenetics and long-read-based analyses are required to uncover the complete chromosomal and genomic organizations of these newly described SatDNA families.

Transposable Elements Structural Sequences Display a Minor Influence on SatDNA Origins in Drosophila
A clear relationship between TE and the origin, amplification, and homogenization of SatDNA has been debated in We scored how many make up ,1% of their resident species genome (,1%), how many make up between 1% and 2%, etc. Most SatDNA have low abundance (,1%) and comprise ,1% of their resident genome (purple).
de Lima and Ruiz-Ruano GBE several insect species, especially in Drosophila (Palomeque and Lorite 2008;McGurk and Barbash 2018). Herein we identified three previously described tandemly repeated families present in the D. virilis group that originated from TEs: pvB370 SatDNA, Tetris-220, and DINE-TR1 CTRs (Heikkinen et al. 1995;Dias et al. 2014Dias et al. , 2015. We identified new SatDNA families that have been annotated as if they share sequence similarity to TEs, two in the D. biarmipes genome and one in the D. pseudoobscura group species (D. pseudoobscura and D. persimilis). The first

GBE
Genome Biol. Evol. 14(5) https://doi.org/10.1093/gbe/evac064 Advance Access publication 3 May 2022 one, DbiaSat2-263, was deposited in RepBase as Copia-2_DTa-I_1p, whereas DbiaSat5-215 was present as Gypsy-11_DEu-I. Despite the annotations, we were unable to find sequence similarity between DbiaSat2-263 and Dbia5-215 and the Copia and Gypsy TE families, respectively. We further identified that the Gypsy18-I_Dpse sequence contains a 228 bp LTR sequence, indicating an incorrect annotation of this specific TE sequence. The Gypsy18-LTR_Dpse sequence deposited in the RepBase database is a large array of a 21 bp SatDNA family shared among D. pseudoobscura and D. persimilis. Further, to confirm our characterization of DbiaSat2-263, Dbia5-215, DperSat3-21, and DpseSat2-21 as SatDNA families that did not originate from TEs, we ran BLASTn searches using each SatDNA consensus sequence as queries against D. biarmipes and D. pseudoobscura group species genomes to obtain tandemly repeated arrays of each family. We analyzed the BLAST results that presented the highest score number; TE was not found in regions immediately flanking the SatDNAs. We identified a single array with 20 monomers of the DbiaSat2-263 family present in tandem on Contig5744 (AFFD02005737.1). We also observed that DbiaSat5-215 showed several contigs composed exclusively of the 215 bp monomers (e.g., AFFD02001879.1; AFFD02002683.1). The same pattern was observed for the two D. pseudoobscura group species (AAIZ01026166.1; AAIZ01022678.1; AADE01010412.1). Our results suggest that both SatDNA families identified in D. biarmipes and the one found in D. pseudoobscura group species do not share homology with TE fragments and are incorrectly annotated as TE elements. Altogether, our data suggest SatDNA originating from TE may be particularly common in D. virilis group species but are mostly absent in the other species examined.

Evolutionary Maintenance of SatDNA Families Supports the SatDNA Library Hypothesis
The SatDNA library hypothesis posits that the genomes of related species would contain similar families of SatDNA (Fry and Salser 1977). This hypothesis also assumes that multiple SatDNA families can coexist within the same genome, forming a collection of repetitive sequences shared among lineages. Although similar SatDNA may be shared between species, the abundance of SatDNA is likely to stochastically change through both expansion and shrinkage in closely related species (Mestrovićet al. 1998). A clear SatDNA library landscape is observed in D. pseudoobscura and D. persimilis genomes which share four SatDNA families; strikingly, the abundance of each SatDNA family varied significantly between the two species, demonstrating the rapid changes in SatDNA content in the genomes of closely related species ( fig. 5A). Two of the four SatDNA families characterized in D. pseudoobscura and D. persimilis (21 and 319 bp) are also present in the D. miranda genome (Mahajan et al. 2018), indicating even longer maintenance of both SatDNA families in this species group. A SatDNA library pattern is also observed in the virilis group, where we confirmed that the SatDNA family pvB370 is conserved for a period of about 20 Myr in the D. virilis group species with different abundances in each species (Heikkinen et al., 1995;Biessmann et al. 2000). We also confirmed the existence of a 172 bp SatDNA family previously described by Abdurashitov et al. (2013) and Silva et al. (2019). Additionally, we described one new SatDNA family with 393 bp shared among the four species of the D. virilis group (supplementary table S2, Supplementary Material online). As expected from the SatDNA library hypothesis, all four SatDNA families comprise different genomic proportions in all four species that diverged 10 Ma, demonstrating that nonhomologous SatDNA families can be shared and independently amplify and contract in each genome ( fig. 5B).
A remarkable example of a SatDNA library pattern was observed between D. albomicans and D. nasuta. Both species share a recent common ancestor (0.5 Ma) but D. albomicans has a very recent neo-Y chromosome system ( 0.1 Ma) (Bachtrog 2006;Wei and Bachtrog 2019;Mai et al. 2020). To better understand the impact of neosex chromosomes in the SatDNA content, we compared both female and male genomes of D. albomicans to D. nasuta and observed significant changes in the total amount of SatDNA sequences, although both species and sexes share the same SatDNA families (the exception is the lowest abundant DnasSat7-444, detected only in D. nasuta genomes) ( fig. 5E and F). We found that D. albomicans and D. nasuta male genomes have the highest SatDNA content 39.1% and 33.9%, followed by 18.1% and 16.5% in females, respectively ( fig. 5E and F). The comparative relative abundance of SatDNA between male and female genomes from D. albomicans and D. nasuta shows that the SatDNA doses differ significantly between sexes for both species, with males harboring a higher copy number than females for most (except for DnasSat2-171 in females, see below) of repeats described.
Finally, Cactophilic Drosophila seems to be the species group with the lowest amount of SatDNA (de Lima et al. 2017). Consistent with the SatDNA library hypothesis, the pool of ancestral SatDNA is maintained throughout this phylogenetic group (fig. 5C). The identification of the pBuM SatDNA family in D. mojavensis, D. arizonae, and D. buzzatii indicates the existence of this SatDNA in the shared common ancestor (12 Ma) of these species (de Lima et al. 2017).

Birth and Expansion of New SatDNA Over Evolutionarily Short Timescales
Despite the maintenance of SatDNA families over prolonged periods, we also observed the independent birth/ expansion of new families and turnover events that have changed the abundance of these sequences over evolutionarily short timescales in Drosophila species. For instance, the closely related species D. orena, D. erecta, D. yakuba, D. teissieri, and D. santomea share a common ancestor 5.7 Ma (David et al. 2007), yet D. orena has the largest genome in the D. melanogaster subgroup (280.7 Mb), almost 100 Mb larger than the D. melanogaster genome ( fig. 2). Interestingly, D. orena shows 6-10-fold more SatDNA content than the other four species. We found that the D. orena genome has a SatDNA family (DoreSat1-172) that represents 10.4% of its genome. DoreSat1-172 sequences can be found in low copy numbers in D. erecta and D. yakuba genomes (,0.01% of each species genome and therefore not fully characterized in this study), indicating that this sequence was present in the common ancestor of these species and passed through a recent expansion only in D. orena ( fig. 5D). Similar patterns of independent SatDNA family birth/expansion were observed between closely related species such as the already cited simulans clade, D. buzzatii/D. seriema, D.albomicans/D. nasuta, and in the D. yakuba species complex (D. yakuba, D. teissieri, and D. santomea), in which each species has species-specific SatDNA families, that is,  de Lima and Ruiz-Ruano not shared between closely related species (supplementary table S2, Supplementary Material online). One notable example of the birth of a new SatDNA sequence is observed for the 193-7 bp SatDNA shared among simulans clade species (Chakraborty et al. 2021). Blast searches in 60 assembled genomes of D. melanogaster using all 193-7 bp consensus as query indicate the presence of one single copy sequence of 192 bp present in a syntenic position on the X chromosome (supplementary material S5, Supplementary Material online). These results suggest that one single copy sequence present on the X chromosome of the simulans clade species common ancestor passed through a recent expansion event. Overall, our results reinforce the idea that SatDNA family births and amplifications are a common feature of eukaryotic genomes and contribute, concomitantly with the SatDNA library, to the dynamics of the genomic landscape of Drosophila species.

Chromosomal Mapping and Validation of 16 Newly Identified SatDNA Throughout Drosophila
To confirm and extend our computational analysis, we experimentally mapped the distribution of 17 newly identified is present in all pericentromeric regions, except the dot chromosomes, whereas DnasSat3-186 and DnasSat5-269 are restricted to the X chromosome distal heterochromatin. We identified DnasSat2-171 signals at telomeric/subtelomeric regions of chromosome 3; however, we could not detect signals for the predicted arrays of DnasSat2-171 present at X or 2R chromosomes. Our phylogenetic analysis using a total of 910 monomers for the DalbSat2-171 and DnasSat2-171 SatDNA families indicated that the chromosome X arrays are significantly different from those arrays in other chromosomes (supplementary material S5, Supplementary Material online). FISH experiments hybridizing chromosome-specific probes will be required to confirm the localization of DnasSat2-171 at X or 2R chromosomes. Our cytogenetic results corroborate the dramatic variation in abundance predicted in silico and validate the characterization of eight simple and nine complex SatDNA families in seven different species.
The Genome Size of Sophophora and Drosophila Subgenera Correlates with Repetitive DNA Content Given the 2.5-fold variation in genome size observed in the Drosophila species analyzed, we expected to find evidence of repetitive elements substantially contributing to nuclear DNA content (Gregory and Johnston 2008;Dodsworth et al. 2014). The SatDNA abundance and the overall repetitive DNA content for 37 species are shown SatDNA content and its contribution to genome size are consistent with previous ideas that genomes have expanded/ contracted mainly by the addition/deletion of repeated sequences (for a review see Gregory 2005). Our estimates suggest that SatDNA sequences comprise a lower proportion of the genome compared with previous analyses in Drosophila (Bosco et al. 2007;Craddock et al. 2016). Prompted by the results described above, we ran statistical analyses using phylogenetic generalized least squares (PGLS) (Revell 2010) to systematically investigate the correlation between SatDNA content, repetitive DNA, and genome size ( fig. 7). We investigated whether the genome size of each subgenus is equally well correlated with SatDNA and overall repetitive content. The positive relationship between genome size and SatDNA is strongly significant in the Drosophila subgenus even after correcting for the phylogenetic groupings. A similar coefficient was observed for SatDNA and repetitive DNA content and the correlation between repetitive DNA content and genome size in the Drosophila subgenus. In contrast, the Sophophora species SatDNA content is less positively correlated with its genome size. The correlation between repetitive DNA and SatDNA content is also low, suggesting that other repetitive sequences, such as TEs, are the major components of genomes in the Sophophora subgenus (supplementary material, Supplementary Material online). The phylogenetic signal is confirmed by generalized least squares analysis for all three datasets and the Brownian model (akaike information criterion [AIC] = 147.02) better fits the genome size evolution association with repetitive DNA and SatDNA than the nonphylogenetic (AIC = 345.5161) or the constrained evolution Ornstein-Uhlenbeck (AIC = 328) model. Our results suggest that genome size evolution in Drosophila can be regulated by both SatDNA and TEs, and the predominant repetitive DNA may depend on the evolutionary backdrop.

Discussion
In the present study, we have performed the largest satellitome identification and characterization in Drosophila species to date. In summary, the de novo characterization of 37 Drosophila satellitomes using the RepeatExplorer pipeline has confirmed the presence of previously identified sequences and described 112 new SatDNAs, significantly broadening the knowledge of SatDNA sequences throughout Drosophila phylogeny. Our results indicate that the Drosophila SatDNA landscape is variable in terms of monomeric size, nucleotide composition, and overall genomic proportion. Notably, most species' genomes contained more than one SatDNA family and many SatDNA families show a low genome proportion, as generally observed in insects (Palomeque and Lorite 2008;Ruiz-Ruano et al. 2018;Palacios-Gimenez et al. 2020). Moreover, we described a trimodal distribution of SatDNA monomer sizes in which the simple SatDNA families (,50 bp) are more frequent than complex SatDNA ( fig. 3A), suggesting that a significant subset of SatDNA monomers is not compatible with the nucleosome wrapping hypothesis (Henikoff et al. 2001). Different features such as secondary or tertiary structures may influence the preferential motif size of these SatDNA families (Garavís et al. 2015;Patchigolla and Mellone 2021). Our results also indicate that simple SatDNA families can comprise a large fraction of a genome (e.g., D. virilis clade, D. hydei, D. albomicans, and D. nasuta). Computational approaches based on k-mer searches might better describe simple SatDNA families/ subfamilies with high sequence similarity (.80%) within a genome as observed in D. melanogaster (see Results; FIG. 7.-PGLS correlation of SatDNA and TEs with genome size in Drosophila genus and both major subgenera Drosophila and Sophophora suggest a differential influence of repetitive components in the genome size variation. PGLS linear regression plots and coefficient between (A) repetitive DNA content and genome size, (B) SatDNA content and repetitive content, and (C) TE content and repetitive Content variation in the Dorsilopha subgenus, and both subgenus Sophophora and Drosophila independently. Regression plots show a significant association of SatDNA variation and genome size evolution in subgenus Drosophila, whereas non-SatDNA repeats (mostly TE) strongly correlate with genome size variation in Sophophora.
de Lima and Ruiz-Ruano supplementary table S3, Supplementary Material online) (Wei et al. 2014. Our analyses suggest that SatDNA originating from TE structural repeats are particularly common in the D. virilis group species but are mostly absent in the other species analyzed. We confirmed the presence of three SatDNA families that originated from TEs in the D. virilis group (pvB370, Tetris-220, and DINE-TR1 CTRs). This finding suggests that amplifications from extant abundant sequences could play a key role in the evolution of the genomic landscape of the D. virilis group for reasons still to be elucidated. Moreover, the "hybrid" structure of mobile elements incorporating tandem repeats in Drosophila, such as Helitrons (Dias et al. 2015), results in complications in the precise classification and quantification of SatDNA sequences (see Results, ŠatovićVukšićand Plohl 2021). Therefore, meticulous sequence characterization of RepeatExplorer outputs is required to surpass this significant classification challenge (Montiel et al. 2021;Cintra et al. 2022).
In this study, we report that SatDNA sequences constitute a variable portion of each Drosophila genome. Genome size and repetitive DNA content covaried across Drosophila phylogeny (Hjelmen and Johnston 2017). SatDNA content and the SatDNA families comprising it were shared among closely related species (figs. 2 and 5). These findings support the SatDNA library hypothesis and show that different SatDNA families coexist within the same genome as part of a collection of repetitive sequences shared among closely related species (Fry and Salser 1977) ( fig. 5). We also discovered multiple independent births and/or expansion events of SatDNA families over evolutionarily short timescales (e.g., D. yakuba; simulans clade; D. buzzatti/D. seriema; D. albomicans/D. nasuta). We acknowledge that the definition of the seminal sequence and amplification history of a SatDNA family represents a challenge to the field and increases the difficulty to test the SatDNA library hypothesis. However, the characterization of SatDNA families that are absent (to the current level of resolution) or lack significant genomic representation (,0.01%) in sister species suggests that the birth or amplification, respectively, of new SatDNA families can occur even throughout short evolutionary timescales in Drosophila. We indicate that the birth of new satDNA families during recent cladogenetic events (as demonstrated in D. simulans clade) can also play a role in the repetitive DNA landscape of a group of species, resulting in an addendum to the satDNA library hypothesis.
Generating the SatDNA content of each species enabled us to analyze the correlation between SatDNA and genome size in Drosophila species. The genome size variation in Drosophila species is postulated to be associated with the amplification/contraction of heterochromatic blocks (Gregory and Johnston 2008;Craddock et al. 2016), comprised mainly of SatDNA arrays and TEs. Our data demonstrate that both SatDNA and non-SatDNA (mostly TEsupplementary material S4, Supplementary Material online) are correlated with genome size ( fig. 2) in line with previous proposals that larger genomes have more repetitive content (Bosco et al. 2007). Here, we described that SatDNA content is highly variable in Drosophila genomes, and this variation roughly follows phylogenetic relationships in which closely related species tend to show a similar SatDNA content. We observed that genome size variation in the Drosophila subgenus shows a strong correlation with the expansion/shrinkage of SatDNA sequences, supporting the hypothesis that SatDNA dynamic changes play a role in genome size evolution (Flynn et al. 2021;Majid and Yuan 2021). Conversely, we described that genome size variation in Sophophora correlates with TE content ( fig. 7; Sessegolo et al. 2016) and that the sum of all repetitive DNA identified in Sophophora does not correlate with the SatDNA abundance (table 2), notably illustrated in D. affinis and D. takahashii genomes ( fig. 2). This particular result suggests that non-SatDNA repetitive sequences, especially TE ( fig. 7; supplementary material S4, Supplementary Material online), constitute a major genomic driver for the genome size variation observed in Sophophora. This observation is consistent with the published finding that chromosome 4 in D. ananassae (subgenus Sophophora) is enlarged due to TE insertions (Leung et al. 2017); interestingly, the most abundant SatDNA in this species is also restricted to the chromosome 4 pair ( fig. 6A). Different expansion/contraction events, such as indels or intron lengths, are also linked to genome size variation in Drosophila (Gregory 2003;Kelly et al. 2015), however, our results suggest that genome size evolution in the Drosophila subgenus is driven by the modulation of SatDNA sequences, whereas TE is a more prevalent driver in Sophophora. The factors driving this difference in these species will be an interesting focus of future investigations, which may include genomic and ecological factors, silencing mechanisms, and host defense systems (Luo and Lu 2017;Funikov et al. 2018;Flynn et al. 2021). It will be fruitful to unveil whether SatDNA/TE content benefits the organism and if genome size variation associated with SatDNA/TE expansions or contractions is influenced by natural selection (Petrov 2001;Graur et al. 2015).
Altogether, we identified 188 SatDNA sequences in 37 species of Drosophila with 112 of them being newly characterized, indicating that SatDNA are underrepresented genomic features that are frequently absent in sequence databases. Despite the advances presented here, our understanding of the full spectrum of SatDNA genomic contribution and organization in Drosophila is far from complete. There are three important limitations to our study. First, genome assemblies generated by NGS technology tend to collapse the number of SatDNA monomers inside arrays and produce a significant number of unmapped contigs. Also, NGS PCR-based sequencing approaches may hinder or underestimate the genomic representation of simple SatDNA due to amplification biases . Further cytogenetics and long-read assembly analyses are still necessary to better understand the organization and distribution of SatDNA sequences. Second, SatDNA sequences are a fast-evolving genomic feature, and studies are limited by the species present in the literature, which may display a limited snapshot of SatDNA sequence evolution in Drosophila. Finally, considerable intraspecific SatDNA variation is observed in eukaryotic genomes (Arora et al. 2021;de Lima et al. 2021), thus future population surveys of genome size variation and repeat composition may be necessary to completely understand the evolution of SatDNA in Drosophila. Our results help reveal how SatDNA evolved and are evolving within the Drosophila genus and serve as an important resource to improve analysis of SatDNA in Drosophila genome assemblies, comparative genomic analyses, and future functional studies.

Genomic Data and Characterization of SatDNA Families
We used genomic Illumina libraries from the 37 Drosophila species publicly available in the EBI Short Read Archive with accession numbers described in supplementary table S1, Supplementary Material Online. Moreover, we retrieved the genome size estimations used in the present study in the Animal Genome Size Database (www.genomesize. com) (Bosco et al. 2007;Hjelmen and Johnston 2017;Hjelmen et al. 2019).
We characterized repetitive elements using the software RepeatExplorer version1 implemented in Galaxy (http:// www.RepeatExplorer.org/) (Novák et al. 2013), which performs a de novo assembly of repetitive elements using a graph-based method to group reads into discrete clusters based on all-by-all BLAST similarity. Before the RepeatExplorer analysis, we trimmed reads from all reads used in this analysis to 100 bp and removed sequencing adapters with Trim Galore! (version 0.6.4_dev; https:// github.com/FelixKrueger/TrimGalore/releases/tag/0.6.4) and Cutadapt (Martin 2011) software, respectively, except for D. mauritiana and D. persimilis, with read lengths of only 76 bp after adapter removal. In addition, we set the cut-off quality value at Q30 and kept only reads with 90% of bases with values higher than this value. Then, as part of the RepeatExplorer analysis, we explicitly chose a stricter clustering threshold of 90% of the read length instead of 65% to uniquely characterize each repetitive sequence. We considered in this study only clusters with genomic proportion equal or higher to 0.01% as recommended by RepeatExplorer authors (Novák et al. 2013). Next, the reads that shared high sequence similarity were clusterized and further aligned and partially assembled to each cluster using CAP3 (parameters: -O -p 80 -o 40; Huang and Madan 1999;Novák et al. 2010). We ran RepeatMasker (Smit et al. 2013(Smit et al. -2015 analyses using read sequences within individual clusters against the Repbase Metazoa database (Bao et al. 2015) to provide information for their annotations. This approach provides information about repeat quantities (estimated from the number of reads that comprise each cluster), the relationship among clusters, and outputs from BLASTn and BLASTx (Altschul et al. 1990) similarity searches on the Repbase database.
Moreover, only clusters consisting of at least 20 reads were considered. This cut-off was low enough to fully capture highly abundant repeats in all species yet remained computationally tractable. We manually curated all clusters using visual inspection of the RepeatExplorer assembled contigs, BLASTn searches at NCBI nt/nr database, and Tandem Repeats Finder (Benson 1999) with default parameters (except max. motif size 2,000 bp), to identify tandemly organized sequences. We generated dot-plots with the Dotlet applet (Junier and Pagni 2000) with a 15 bp word size and 60% similarity cut-off. Following the tandem repeats identification step, we ran a second round of RepeatMasker searches using the Drosophila database (https://www.girinst.org/censor/index.php) to characterize possible TE internal repeats. Manual curation was also required to exclude repetitive gene families (e.g., histone genes, rDNA, etc.), mitochondrial and microbial contaminants. To confirm the presence of tandemly repeated arrays in the genome of Drosophila species, all SatDNA consensus sequences identified by RepeatExplorer methods were used as a query for BLASTn searches on assembled genome data from the NCBI whole genome sequences database. To examine patterns on a broader scale, we also mined each SatDNA sequence and performed alignment analyses after a random generator (www.random.org) to select 20 contigs containing arrays retrieved from BLASTn output with e-value lower than 10 −5 , when available. The nomenclature of all new SatDNA was assigned as suggested by Ruiz-Ruano et al. (2016), which includes: species name abbreviation, number of decreasing abundance order, and the repeat unit size. Thus, we characterized all tandemly arranged sequences in this study after confirming the period size and the nucleotide structure, as well as, discarding other repetitive sequences. This manual annotation was crucial to the reliability of each SatDNA description.
To search for homologous SatDNA between different species, we built a SatDNA database by concatenating all the SatDNA consensus sequences detected in each species and performed an all-against-all comparison of the consensus sequences in the database using the rm_homology.py script (https://github.com/fjruizruano/ngs-protocols). Further, we confirmed independently the repetitive DNA profiles of SatDNA sequences identified by RepeatExplorer methods using the RepeatProfiler software (github.com/ johnssproul/RepeatProfiler) (Negm et al. 2020) with the same set of sequence read archive files (supplementary table S1, Supplementary Material online) with the previous cut-off quality values, trimming, and adapter removal options. We used RepeatProfiler with default parameters for generating, visualizing, and comparing repetitive DNA profiles from low-coverage, short-read sequence data. Due to restrictions in the pipeline, all SatDNA consensuses were artificially arranged in tandem to form input sequences equal to or longer than 100 bp.
We classified RepeatExplorer clusters with their annotation as Satellites, those used to characterize a SatDNA, multigenic families with homology with a multigenic family gene (see below), and the remaining ones were considered as transposable elements. To characterize clusters of multigenic families, we searched for homology with those genes with RepeatMasker in the contigs of RepeatExplorer assemblies with a proportion equal to or higher than 0.01% as we did previously. As a reference, we used sequences of D. melanogaster downloaded from GenBank. We used the genes of ribosomal DNAs (accession numbers M21017.1 and NR_001793 for the 45S and 5S rDNAs), U snRNAs (accession numbers NR_001599, NR_002513, NR_001670, NR_001933, and NR_002081 for U1, U2, U4, U5, and U6, respectively) and histones (accession number X14215), as well as the whole mitochondrial genome (accession number NC_024511). Because these genes are very conserved, they are valid to get homology based on nucleotides in all the species included in this study. We accepted annotations when the coverage of the contigs matching with the reference covered at least 50% of all read of its RepeatExplorer cluster.

Alignment and Phylogenetic Reconstruction
We aligned a pool of nucleotide sequences retrieved from assembled genomes (when available) for each SatDNA family using MUSCLE (Edgar 2004) integrated into the MEGA7 platform (Kumar et al. 2016). We added the previously described sequences from Drosophila SatDNA (when available, supplementary material S3, Supplementary Material online) to confirm the sequence previously described initial nucleotides of SatDNA families present on other organisms available in GenBank (supplementary material S3, Supplementary Material online). We determined the nucleotide evolution model to be used in the phylogenetic reconstructions using jModelTest2 (Darriba et al. 2012). This analysis allowed us to determine the evolutionary model T93 as the most common model to best explain our data. Finally, we performed Neighbor-Joining phylogenetic reconstructions using MEGA 7 (Kumar et al. 2016) with 1,000 bootstrap replicates and applied the T93 evolutionary model.

Mitotic Chromosomes Preparations and In Situ Hybridization Experiments
Mitotic chromosome preparations and FISH experiments were conducted as described in de Lima et al. (2017). The brains from third-instar larvae were dissected in 0.7% sodium chloride and moved to a fresh 20 µl drop of Colcemid (Roche) in darkness, followed by 0.5% sodium citrate hypotonic treatment for 10 min and then fixated for 2 min in 100 µl of fixative solution (45% acetic acid, 100% ethanol). After fixation, the brains were transferred to a 60 µl drop of 60% acetic acid on heated to 65°C on a heat block for 5 min or until dry. For FISH, each dried slide was fixed with paraformaldehyde 4% solution for 30 min and then transferred to 100% ethanol. Slides were then incubated in 2× SSC at 65°C for 30 min and dehydrated in 70% and 96% ethanol for 10 min each. Chromosomes were denatured in 0.07 M NaOH solution for 30 s and immediately incubated in 2× SSC solution for 10 min. Slides were then dehydrated in two consecutive 2 min incubations of 70% and 96% ethanol. For each slide, 20 µl of the FISH solution (50% formamide, 10% dextran sulfate, 2× SSC, 100 ng fluorescence-labeled probe) was applied directly to the dried slide and a clean Parafilm 22 mm × 22 mm coverslip was placed on top. All probes used are described in supplementary table S6, Supplementary Material online. Slides were incubated at 37°C overnight (16-24 h). After the incubation, slides were washed twice in 2× SSC at 37°C for 5 min each. Slides were then washed three times in SSCT (4× SSC, 0.1% Tween-20), followed by two washes in 1× PBS; each wash was 5 min. Excess liquid was wiped from around the sample area, and each slide was mounted with 5 µl Vectashield + DAPI and a 22 mm × 22 mm coverslip. Coverslip edges were sealed with nail polish and imaged immediately. Chromosome images were acquired with an Inverted Zeiss LSM 780 confocal microscope. All imaging used a 63× objective for mitotic chromosomes. Stacks of deconvolved images were combined in a z-projection showing maximum intensity, cropped to the region of interest, recolored, and adjusted for brightness and contrast in FIJI/ ImageJ.

Statistical Analyses
To infer the correlations between each feature analyzed (genome size, SatDNA content, non-SatDNA content, and overall repetitive DNA content), we used the Spearman's correlation coefficient (ρ). This correlation test was used due to the nonparametric nature of genome size and repetitive DNA evolution. Thus, the general form of a null hypothesis for a Spearman correlation is given by H0: there is no monotonic association between the two variables. The ρ can be calculated by r = i (x i − x)(y i − y) i (x i − x) 2 i (y i − y) 2 , where i is the paired score. Statistical analyses were run using PRISM 9 software. Further, we incorporated phylogenetic information to correlations between repeat abundances and genome size using PGLS with the maximum likelihood method implemented in the nlme R package (Pinheriro et al 2020).

Supplementary Material
Supplementary data are available at Genome Biology and Evolution online.


s2fieldsofstudy: Biology Medicine

text:
Rapid identification of carbapenemases by CarbAcineto NP test and the rate of beta-lactamases among Acinetobacter baumannii from a teaching hospital

Background and Objectives: Acinetobacter baumannii has emerged as a major organism accounting for hospital acquired infections particularly in intensive care units. Due to production of different kinds of beta lactamases these bacteria have developed drug resistance rendering the treatment of such infections very difficult and expensive. Rapid identification of A. baumannii producing such beta-lactamases is the need of the hour in reducing morbidity and mortality associated with A. baumannii infections. Materials and Methods: A. baumannii was isolated from clinical samples like endotracheal aspirates, sputum, urine, exudates using standard culture techniques. Identification and drug sensitivity was done using Vitek 2 system. All the isolates were subjected to detection of ESBLs using phenotypic confirmatory test, plasmid mediated AmpC beta-lactamase by AmpC disc test, Carbapenemase production by CarbAcineto NP Test and Modified hodge method. Results: 149 A. baumannii isolates were analysed for antimicrobial susceptibility and various beta-lactamase production. Results were evaluated for statistical significance using Chi-Square and P value. 81.8% of isolates were from male patients with majority of them above 50 years of age. 88.5% of samples were from ventilator associated pneumonia patients. 83.8% of isolates were sensitive to tigecycline. Only 10% to 12% of isolates were sensitive to carbapenems. 23.4% of isolates were ESBL producers and 46.9% of them were AmpC producers. Modified Hodge test method identified 63.7% of A. baumannii as carbapenemase producers where as CarbAcineto NP test identified 63% and exibiting 94.74% sensitivity, 93.22% specificity when compared to Modified Hodge test. Conclusion: Multidrug resistant Acinetobacter spp. is on the rise. Present study showed that high percentage of drug resistance in A. baumannii could be due to production of ESBLs, AmpC and carbapenemases. Among all beta lactamases carbapenemase producers are more and quickly raising in A. baumannii. Rapid, cost effective assay which can be adopted in all clinical laboratories is critical to prevent their further transmission particularly in hospital environment.

A. baumannii infections are frequently treated with β-lactam antibiotics like cephalosporins and carbapenems or with other group of antimicrobials like aminoglycosides, polymyxins and glycylcycline. Majority of A. baumannii strains have developed resistant to most of these antibacterial agents due to the presence of mobile genetic elements, such as insertion sequences (ISs), plasmids, integrons and resistance islands (2). The most important mechanism of β-lactam resistance in A. baumannii is production of various types of beta-lactamases like extended spectrum betalactamases (ESBLs) , AmpC and carbapenemases. The Ambler classification divides the β-lactamases into four major molecular classes (A-D). The extended-spectrum β-lactamases (ESBLs) belong to class A and can hydrolyze first, second and third-generation of Cephalosporins, Penicillins, and monobactams but are inhibited by clavulanate (3). AmpC β-lactamases production is mediated either by chromosomes or by plasmids and cannot be inhibited by clavulanic acid, but can be inhibited by cloxacillin. The enzymes belong to the molecular class C (4). Chromosomally encoded AmpC beta-lactamases in A. baumannii have a low level of expression that does not cause clinically appreciable resistance, however, the addition of a promoter insertion sequence ISA-ba1 next to the ampC gene increases beta-lactamase production, causing resistance to cephalosporins (5). Plasmid mediated AmpC and ESBLs can effortlessly move from one bacteria to another resulting in the faster spread of the resistance genes (2). Due to the emergence of ESBLs and AmpC beta-lactamase, Carbapenems like imipenem, meropenem are used extensively to treat infections in hospitalized patients. This resulted in unresponsiveness among bacteria to carbapenems. carbapenem resistance in A. baumannii is due to a range of mechanisms like decreased expression of outer membrane proteins and production of carbapenemases (6). Increasing proportion of A. baumannii producing carbapenemases has resulted in growing therapeutic concern worldwide due to very few treatment options available right now. The rapid identification of such isolates is the need of the hour to manage infections associated with such organisms and to prevent their spread (7). This study was carried out with an intention to quickly identify the alarmingly emerging carbapenemases and also to know the rate of ESBL and AmpC beta-lactamase from A. baumannii.

MATERIALS AND METHODS
The present study was carried out in a tertiary care teaching hospital for a period of one year. A total of 149 A. baumannii isolates were collected from clinical samples like endotracheal aspirates, sputum, urine, exudates etc from patients admitted during study period. Isolation of A. baumannii was done by standard culture techniques by inoculating clinical samples on to blood agar and MacConkey agar and incubating aerobically at 37°C.
Identification and drug sensitivity of A. baumannii was done using Vitek 2 system according to the manufacturer's guidelines. All the isolates were subjected to phenotypic detection of ESBL, AmpC and Carbapenemase.

ESBL detection.
A lawn culture of A. baumannii was done on quality control passed and sterility checked Mueller Hinton agar plate. A sterile disc of ceftazidime (30 μg) and ceftazidime and clavulanic acid (30 μg/10 μg) (Himedia) was applied at a distance of 20 mm from centre to centre on Mueller Hinton agar plate, and incubated at 37°C for 18 to 24 hours. A difference in the zone of more than or equal to 5 mm between the two discs was considered as ESBL producer (1).
AmpC disc test. Using 0.5 McFarland's matched broth of ATCC E. coli 252922 a lawn culture was done with sterile cotton swab. A sterile 30 µg cefoxitin disc (Himedia) was kept on agar surface and a blank sterile disc was moistened with sterile saline and was inoculated with few colonies of A. baumannii isolate and placed touching the cefoxitin disc. The plates were incubated at 37°C for 18 to 24 hours. The organism was considered as AmpC producer if flattening or indentation of inhibition zone of cefoxitin disc in the vicinity of the disc with A. baumannii (8).
Carbapenemase production: modified Hodge method. Using 0.5 McFarland's matched broth of ATCC E. coli 252922 a lawn culture was done with sterile cotton swab. 10μg meropenem disc (Himedia) was placed at the centre of the agar plate and A. baumannii isolate was streaked from the disc's edge to the periphery of the plate. The plate was then incubat-ed at 37°C for 18 to 24 hours. Appearance of a clover leaf shaped zone of inhibition along the growth of A. baumannii indicates carbapenemase production (9).
CarbAcineto NP. From overnight growth of A. baumannii on Mueller-Hinton agar (MHA) two to three colonies were placed in two 1.5-ml Eppendorf tubes (A and B) containing 100 µl of 5 M NaCl and mixed using a vortexer for 5 s. Both the tubes were mixed with 100 µl of a pH adjusted 0.05% phenol red containing 0.1 mmol/liter ZnSO4. Then to the tube B 12 mg/ml Imipenem-cilastatin injectable drug was added. Both the tubes were incubated at 37°C for 2 hours. The carbapenemase produced by the bacterial strains could hydrolyze the imipenem into a carboxylic derivative and result in decrease in pH. This reduction in pH is indicated by color change of the phenol red solution. Any change in color from red to orange/yellow in the antibiotic containing tube (tube B) indicates carbapenemase activity (9).

RESULTS
During study period, 149 A. baumannii non repetitive clinical isolates were analysed for antimicrobial susceptibility and various beta-lactamase production. Results were evaluated for statistical significance using Chi-Square and P value.
Out of 149 isolates, 122 (81.8%) were from male patients and 28 (18.7%) were from female patients. Maximum number of isolates studied were from patients in the age group of 41-50 (20%) and 51-60 years (18.6%). Age wise and gender wise distribution of patients from whom these clinical isolates grown is shown in Table 1. Table 1. Age and sex wise distribution of the cases Maximum number of clinical isolates were from endotracheal aspirates (88.5%) of patients with suspected ventilator associated pneumonia (VAP) followed by sputum from patients diagnosed with nosocomial pneumonia and exudates samples (4.6%) two each from diabetic wound and burns wound. Distribution of isolates according to sample source is shown in Table 2.
Out of 132 patients with suspected VAP 84 were admitted in neuro ICU, 37 were admitted in RICU and 11 patients were in MICU. Piperacillin-tazobactam, meropenem or imipenem were predominant antibiotics started in these patients. Later most of these patients were started with tigecycline or colistin based on antimicrobial susceptibility which was performed using N-281 card of Vitek 2 system. Majority of the isolates were resistance to commonly used cephalosporins like ceftazidime, cefipime. Only 10% to 12% of isolates were sensitive to carbapenems like imipenem, meropenem and doripenem. The most susceptible drug was found to be the tigecycline (83.8%) followed by minocycline (61.7%). Antimicrobial resistance pattern of these isolates is depicted in Table 3. Out of 149 A. baumannii only 23.4% of isolates were ESBL producers where as 46.9% of them were AmpC producers. Distribution of these beta-lactamases is shown in Table 4

DISCUSSION
A. baumannii belongs to the Moraxellaceae family commonly associated with infections like hospital-acquired and ventilator-associated pneumonia, urinary tract infections and pyogenic infections. A. baumannii is one of the ESKAPE organisms that pose a global threat to human health (5).
A. baumannii has developed resistance to many groups of antibiotics due to indiscriminate use of antimicrobials (10). One of the major concerns is development of resistance to beta-lactam group of antibiotics as this result in restricted treatment options for treating A. baumannii infections and can contribute to increased morbidity and mortality in ICU patients (11). The overall prevalence of multidrug-resistant strains in patients with A. baumannii hospital-acquired and ventilator-associated pneumonia is estimated to be 79.9%, ranging from 56.5% in Argentina and 61.8% in Taiwan to 100% in Central America, Pakistan, Lebanon, Qatar, and Croatia, while its overall mortality can be as high as 56.2%. Carbapenems were the treatment of choice for multidrug resistant A. baumannii infections. Their extensive use has led to increased incidence of carbapenem resistance in recent years (5). One of the frequent ways of developing resistance to carbapenems in A. baumannii is production of carbapenemase especial-ly Ambler class D β-lactamases and to a lesser extent class B β-lactamases [metallo-β-lactamases (MBLs)] which includes four types like IMP, VIM, NDM, and SIM. Most of the class D β-lactamases are not inhibited by β-lactamase inhibitors (12). Other types of beta-lactamases in A. baumannii includes Class A beta-lactamases like ESBLS, KPCs and Class C beta-lactamases, which are chromosomally encoded cephalosporinases (Acinetobacter derived cephalosporinase, ADC. Carbapenem resistant A. baumannii was ranked in 2018 by WHO as number one priority for antibiotic research and development (5). So need of the hour is rapid identification of A. baumannii producing such carbapenemase. In the present study around 149 A. baumannii were screened for rapid identification of carbapenemase and also other beta lactamases like ESBLs and AmpC.
In the present study 122 (81.8%) clinical isolates included were from male patients with highest number of isolates from patients above 50 years of age. In accordance to present study TrangDinhVan et al. also observed male predominance (72.7%) and aged above 50 years (13). These findings suggest that male elderly were more vulnerable for infections in both the studies. 88% of A. baumannii during study period were from endotracheal aspirates followed by exudate samples. In a study done by Uzma Rehman et al. also reported similar findings (8). These findings highlights that A. baumannii is the predominant organism causing ventilator associated pneumonia.
In this study 83.8% of A. baumannii were sensitive to tigecycline. Sensitivity to carbapenems ranged between 10% to 12%. Michelle Lowe et al. from South Africa also, reported carbapenem sensitivity in the range of 11% to 13% and 90% of the isolates were resistant to tigecycline (12). In a study from Lebanon, Micheline Hajjar Soudeiha et al. reported carbapenem sensitivity between 16% to 23% and only one isolate was reported as resistant to colistin (14). These findings suggest that antimicrobial susceptibility patterns vary from place to place. Since present study was done in a tertiary care hospital where the patients are referred who are terminally ill and treated with broad spectrum antibiotics elsewhere could be the reason behind increased resistance to higher antibiotics.
ESBL identification in Acinetobacter spp. is not easy as standard protocols are not available unlike in Enterobacteraceae (15). 23 (23). Phenotypic detection of carbapenemases has the advantages of low cost, ease of procedure and the absence of complicated or expensive equipment; however, it suffers from poor specificity and sensitivity unlike PCR. So it is better to use more than one method for not missing out carbapenemase producers however it may not be possible to adapt all the different methods in routine laboratory practice. It is better to use more rapid, reliable, cost effective methods like CarbAcineto NP or modified carbapenem inactivation method (mCIM) methods. One of the limitations of the present study is not evaluating sensitivity and specificity of phenotypic methods by genotypic detection methods due to cost constraints.

CONCLUSION
Acinetobacter are "superbugs" causing infections in the intensive care units. Multidrug resistant Acinetobacter spp. is on the rise making the treatment more difficult. The present study showed that high percentage of drug resistance in A. baumannii and also prevalence of ESBLs, AmpC and carbapenemase among these isolates is high. Rapid, cost effective, assay which can be adopted in all clinical laboratories is critical to prevent their further transmission particularly in hospital environment.